Estimation of Copper and Molybdenum Grades and Recoveries in the Industrial Flotation Plant Using the Artificial Neural Network

In this paper, prediction of copper and molybdenum grades and their recoveries of an industrial flotation plant are investigated using the Artificial Neural Networks (ANN) model. Process modeling has done based on 92 datasets collected at different operational conditions and feed characteristics. The prominent parameters investigated in this network were pH, collector, frother and F-Oil concentration, size percentage of feed passing 75 microns, moisture content in feed, solid percentage, and grade of copper, molybdenum, and iron in feed. A multilayer perceptron neural network, with 10:10:10:4 structure (two hidden layers), was used to estimate metallurgical performance. To obtain the optimal hidden layers and nodes in a layer, a trial and error procedure was done. In training and testing phases, it achieved quite correlations of 0.98 and 0.93 for Copper grade, of 0.99 and 0.92 for Copper recovery, of 0.99 and 0.92 for Molybdenum grade and of 0.99 and 0.94 for Molybdenum recovery prediction, respectively. The proposed neural network model can be applied to determine the most beneficial operational conditions for the expected Copper and Molybdenum grades and their recovery in final concentration of the industrial copper flotation process.

Silicon Mediated Arsenic Reduction in Rice by Limiting Its Uptake

This study was planned to examine the effects of exogenous silicon supply on growth parameters and arsenic accumulation level in rice. The experiment was conducted in the wire house of Saline Agriculture Research Centre, Institute of Soil and Environmental Science, University of Agriculture Faisalabad. The study was comprised of treatments viz: control;(100 μM Arsenic);(200 μM Arsenic);(5 mM Silicon);(5 mM Silicon + 100 μM Arsenic) and (5 mM Silicon + 200 μM Arsenic). Results revealed that maximum shoot fresh weight, shoot dry weight, root fresh weight and root dry weight were observed in (5 mM Si) solution. In the same way, maximum number of tillers was also recorded in (5 mM Si) solution;while silicon application failed to alleviate arsenic concentration of rice genotype.

Development of Sensible Heat Storage Materials Using Sand, Clay and Coal Bottom Ash

In this paper, the mechanical and thermal properties of a sand-clay ceramic with additives coal bottom ash (CBA) waste from incinerator coal power plant are investigated to develop an alternative material for thermal energy storage (TES). Ceramic balls are developed at 1000°C and 1060°C using sintering or firing method. The obtained ceramics were compressed with a compression machine and thermally analyse using Decagon devise KD2 Pro thermal analyser. A muffle furnace was also used for thermal cycling at 610°C. It was found that the CBA increased the porosity, which resulted in the increase of the axial tensile strength reaching 3.5 MPa for sand-clay and ash ceramic. The ceramic balls with the required tensile strength for TES were selected. Their volumetric heat capacity, and thermal conductivity range respectively from 2.4075 MJ·m-3·°C-1 to 3.426 MJ·m-3·°C-1 and their thermal conductivity from 0.331 Wm-1·K-1, to 1.014 Wm-1·K-1 depending on sand origin, size and firing temperature. The selected formulas have good thermal stability because the most fragile specimens after 60 thermal cycles did not present any cracks. These properties allow envisioning the use of the ceramic balls developed as filler material for thermocline thermal energy storage (structured beds) in Concentrating Solar Power plants. And for other applications like solar cooker and solar dryer.

Concentration-and flux-based dose-responses of isoprene emission from poplar leaves and plants exposed to an ozone concentration gradient

Supported by the National Key Research and Development Program of China,Key Research Program of Frontier Sciences,CAS,the Hundred Talents Program,Chinese Academy of Sciences(CAS),and National Natural Science Foundation of China,a cooperative study by the research groups led by Prof.

A New Critical Nitrogen Dilution Curve for Rice Nitrogen Status Diagnosis in Northeast China

In-season diagnosis of crop nitrogen(N) status is crucial for precision N management. Critical N(N_c) dilution curve and N nutrition index(NNI) have been proposed as effective methods to diagnose N status of different crops. The N_c dilution curves have been developed for indica rice in the tropical and temperate zones and japonica rice in the subtropical-temperate zone, but they have not been evaluated for short-season japonica rice in Northeast China. The objectives of this study were to evaluate the previously developed N_c dilution curves for rice in Northeast China and to develop a more suitable N_c dilution curve in this region. A total of17 N rate experiments were conducted in Sanjiang Plain, Heilongjiang Province in Northeast China from 2008 to 2013. The results indicated that none of the two previously developed N_c dilution curves was suitable to diagnose N status of the short-season japonica rice in Northeast China. A new N_c dilution curve was developed and can be described by the equation N_c = 27.7 W^(-0.34) if W ≥ 1 Mg dry matter(DM) ha^(-1) or N_c = 27.7 g kg^(-1) DM if W < 1 Mg DM ha^(-1), where W is the aboveground biomass. This new curve was lower than the previous curves. It was validated using a separate dataset, and it could discriminate non-N-limiting and N-limiting nutritional conditions. Additional studies are needed to further evaluate it for diagnosing N status of different rice cultivars in Northeast China and develop efficient non-destructive methods to estimate NNI for practical applications.